"""
FAISS-based semantic memory for task history and retrieval.

Features:
- Cosine similarity search via FAISS IndexFlatIP
- Top-k retrieval with configurable threshold
- Persistent save/load to disk
- Deduplication via embedding similarity
- Metadata storage alongside vectors
"""

try:
    from .embeddings import EmbeddingModel
except ImportError:
    # Fallback for direct module loading
    from embeddings import EmbeddingModel

import faiss
import numpy as np
from typing import List, Dict, Any, Optional
import pickle
import os
import logging

logger = logging.getLogger(__name__)


class AgentMemory:
    """
    FAISS-based semantic memory for task history and retrieval.

    Features:
    - Cosine similarity search (IndexFlatIP)
    - Top-k retrieval with threshold
    - Persistent save/load
    - Deduplication via embedding similarity

    Example:
        >>> embedder = EmbeddingModel()
        >>> memory = AgentMemory(embedder=embedder)
        >>> memory.add("Calculate 2+2", "4")
        >>> results = memory.search("What is 2+2?")
        >>> print(results[0]["result"])  # "4"
    """

    def __init__(
        self,
        dimension: int = 384,
        k: int = 5,
        similarity_threshold: float = 0.75,
        dedup_threshold: float = 0.95,
        embedder: Optional[EmbeddingModel] = None
    ):
        """
        Initialize FAISS memory system.

        Args:
            dimension: Embedding dimension (default 384 for MiniLM-L6-v2)
            k: Number of results to retrieve
            similarity_threshold: Minimum cosine similarity for retrieval (0.0-1.0)
            dedup_threshold: Cosine similarity threshold for deduplication (0.0-1.0)
            embedder: Optional EmbeddingModel instance (creates new if None)
        """
        self.k = k
        self.similarity_threshold = similarity_threshold
        self.dedup_threshold = dedup_threshold

        # Initialize embedder
        if embedder is None:
            logger.info("Creating new EmbeddingModel for memory")
            self.embedder = EmbeddingModel()
            self.dimension = self.embedder.dimension
        else:
            self.embedder = embedder
            self.dimension = dimension

        # Initialize FAISS index (cosine similarity via inner product)
        # Note: sentence-transformers already normalizes embeddings, so IndexFlatIP = cosine similarity
        self.index = faiss.IndexFlatIP(self.dimension)

        # Store metadata for each vector
        self.metadata: List[Dict[str, Any]] = []

        logger.info(
            f"AgentMemory initialized (dim={self.dimension}, k={k}, "
            f"threshold={similarity_threshold}, dedup={dedup_threshold})"
        )

    def add(
        self,
        task: str,
        result: Any,
        metadata: Optional[Dict[str, Any]] = None,
        skip_dedup: bool = False
    ) -> bool:
        """
        Add task-result pair to memory with deduplication.

        Args:
            task: Task description or query
            result: Task result or answer
            metadata: Optional metadata dict (e.g., execution_time, tokens)
            skip_dedup: Skip deduplication check (for bulk loading)

        Returns:
            True if added, False if duplicate

        Example:
            >>> memory.add(
            ...     task="Calculate 2+2",
            ...     result="4",
            ...     metadata={"execution_time": 0.5}
            ... )
            True
        """
        # Check for near-duplicates
        if not skip_dedup and self.is_duplicate(task):
            logger.debug(f"Skipping duplicate task: {task[:50]}...")
            return False

        # Embed task (already normalized by sentence-transformers)
        embedding = self.embedder.embed_single(task)

        # Ensure normalization (defensive - sentence-transformers should already normalize)
        norm = np.linalg.norm(embedding)
        if norm > 0:
            embedding = embedding / norm

        # Add to FAISS
        self.index.add(np.array([embedding], dtype=np.float32))

        # Store metadata
        meta = {
            "task": task,
            "result": str(result)[:1000],  # Truncate to prevent memory bloat
            "metadata": metadata or {}
        }
        self.metadata.append(meta)

        logger.debug(f"Added to memory: {task[:50]}... (total: {self.index.ntotal})")
        return True

    def search(
        self,
        query: str,
        k: Optional[int] = None,
        threshold: Optional[float] = None
    ) -> List[Dict[str, Any]]:
        """
        Search memory for similar tasks.

        Args:
            query: Search query
            k: Number of results (default: self.k)
            threshold: Similarity threshold (default: self.similarity_threshold)

        Returns:
            List of dicts with keys: task, result, similarity, metadata

        Example:
            >>> results = memory.search("What is 2+2?", k=3)
            >>> for r in results:
            ...     print(f"{r['task']} -> {r['result']} (sim={r['similarity']:.2f})")
        """
        if self.index.ntotal == 0:
            logger.debug("Memory is empty, no results")
            return []

        k = k or self.k
        threshold = threshold or self.similarity_threshold
        k = min(k, self.index.ntotal)

        # Embed and normalize query
        query_embedding = self.embedder.embed_single(query)
        norm = np.linalg.norm(query_embedding)
        if norm > 0:
            query_embedding = query_embedding / norm

        # Search FAISS
        similarities, indices = self.index.search(
            np.array([query_embedding], dtype=np.float32),
            k
        )

        # Filter by threshold and format results
        results = []
        for similarity, idx in zip(similarities[0], indices[0]):
            if similarity >= threshold:
                meta = self.metadata[idx]
                results.append({
                    "task": meta["task"],
                    "result": meta["result"],
                    "similarity": float(similarity),
                    "metadata": meta.get("metadata", {})
                })

        logger.debug(f"Memory search: {len(results)}/{k} results above threshold {threshold}")
        return results

    def is_duplicate(
        self,
        task: str,
        threshold: Optional[float] = None
    ) -> bool:
        """
        Check if task is very similar to existing tasks.

        Args:
            task: Task to check
            threshold: Similarity threshold (default: self.dedup_threshold)

        Returns:
            True if duplicate found, False otherwise

        Example:
            >>> memory.add("Calculate 2+2", "4")
            >>> memory.is_duplicate("Calculate 2+2")  # True
            >>> memory.is_duplicate("What is 3+3?")   # False
        """
        if self.index.ntotal == 0:
            return False

        threshold = threshold or self.dedup_threshold

        # Search with k=1
        similar = self.search(task, k=1, threshold=threshold)

        is_dup = len(similar) > 0 and similar[0]["similarity"] >= threshold
        if is_dup:
            logger.debug(f"Duplicate detected (sim={similar[0]['similarity']:.3f}): {task[:50]}...")

        return is_dup

    def clear(self):
        """
        Clear all memory.

        Example:
            >>> memory.clear()
            >>> memory.get_stats()["total_items"]
            0
        """
        self.index.reset()
        self.metadata.clear()
        logger.info("Memory cleared")

    def save(self, path: str):
        """
        Save memory to disk.

        Args:
            path: Base path (will create .index and .meta files)

        Example:
            >>> memory.save("cache/agent_memory")
            # Creates: cache/agent_memory.index, cache/agent_memory.meta
        """
        # Create directory if needed
        os.makedirs(os.path.dirname(path) if os.path.dirname(path) else ".", exist_ok=True)

        # Save FAISS index
        faiss.write_index(self.index, f"{path}.index")

        # Save metadata
        with open(f"{path}.meta", "wb") as f:
            pickle.dump({
                "metadata": self.metadata,
                "config": {
                    "dimension": self.dimension,
                    "k": self.k,
                    "similarity_threshold": self.similarity_threshold,
                    "dedup_threshold": self.dedup_threshold
                }
            }, f)

        logger.info(f"Memory saved to {path} ({self.index.ntotal} items)")

    def load(self, path: str):
        """
        Load memory from disk.

        Args:
            path: Base path (will load .index and .meta files)

        Example:
            >>> memory = AgentMemory()
            >>> memory.load("cache/agent_memory")
        """
        if not os.path.exists(f"{path}.index"):
            logger.warning(f"Memory file not found: {path}.index")
            return

        if not os.path.exists(f"{path}.meta"):
            logger.warning(f"Metadata file not found: {path}.meta")
            return

        try:
            # Load FAISS index
            self.index = faiss.read_index(f"{path}.index")

            # Load metadata
            with open(f"{path}.meta", "rb") as f:
                data = pickle.load(f)
                self.metadata = data["metadata"]

                # Load config if available
                if "config" in data:
                    config = data["config"]
                    self.dimension = config.get("dimension", self.dimension)
                    self.k = config.get("k", self.k)
                    self.similarity_threshold = config.get("similarity_threshold", self.similarity_threshold)
                    self.dedup_threshold = config.get("dedup_threshold", self.dedup_threshold)

            logger.info(f"Memory loaded from {path} ({self.index.ntotal} items)")

        except Exception as e:
            logger.error(f"Failed to load memory from {path}: {e}", exc_info=True)
            raise

    def get_stats(self) -> Dict[str, Any]:
        """
        Get memory statistics.

        Returns:
            Dict with memory stats

        Example:
            >>> stats = memory.get_stats()
            >>> print(f"Total items: {stats['total_items']}")
        """
        return {
            "total_items": self.index.ntotal,
            "dimension": self.dimension,
            "k": self.k,
            "similarity_threshold": self.similarity_threshold,
            "dedup_threshold": self.dedup_threshold
        }

    def get_all_tasks(self) -> List[str]:
        """
        Get all task strings in memory.

        Returns:
            List of task strings

        Example:
            >>> tasks = memory.get_all_tasks()
            >>> print(f"Memory contains {len(tasks)} tasks")
        """
        return [meta["task"] for meta in self.metadata]

    def __len__(self) -> int:
        """Get number of items in memory."""
        return self.index.ntotal

    def __repr__(self) -> str:
        """String representation."""
        return f"AgentMemory(items={self.index.ntotal}, dim={self.dimension}, k={self.k})"